A foundation model is a deep learning algorithm that has been pre-trained with extremely large data sets scraped from the public internet. Unlike narrow artificial intelligence ( narrow AI ) models ...

Pre-trained foundation models (FMs), with extensive number of neurons, are key to advancing next-generation intelligence services, where personalizing these models requires massive amount of ...

In seismology, while training a specific deep learning model for each task is common, it often faces challenges such as the scarcity of labeled data and limited regional generalization. Addressing ...

Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. LLaMA was released at several sizes, along with a model card that details ...

As reported by HPCwire, a new paper discuses the concept of “catastrophic overtraining,” whereby extended pre-training can harm a model’s performance after fine-tuning.

The foundation model approach offers several key advantages for modelling the Earth System: Leveraging diverse data: By training on vast amounts of varied weather and climate data during pre-training, ...

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

Foundation models, being pre-trained, significantly reduce these costs and can be deployed much faster. • Access To Cutting-Edge Technology: Foundation models are often developed by leading AI ...

Foundation models such as OpenAI's ChatGPT are pre-trained on vast data sets and provide a general basis for developers to build more specialist models without such extensive training. A team led by ...